Machine Learning (ML) has replaced traditional handcrafted methods for perception and
prediction in autonomous vehicles. Yet for the equally important planning task, the adoption …

Real-world autonomous driving systems must make safe decisions in the face of rare and
diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world …

Learning-based approaches to autonomous vehicle planners have the potential to scale to
many complicated real-world driving scenarios by leveraging huge amounts of driver …

In this work, we propose the world's first closed-loop ML-based planning benchmark for
autonomous driving. While there is a growing body of ML-based motion planners, the lack of …

Our goal is to train a neural planner that can capture diverse driving behaviors in complex
urban scenarios. We observe that even state-of-the-art neural planners are struggling to …

The release of nuPlan marks a new era in vehicle motion planning research, offering the first
large-scale real-world dataset and evaluation schemes requiring both precise short-term …

Although planning is a crucial component of the autonomous driving stack, researchers
have yet to develop robust planning algorithms that are capable of safely handling the …

In recent years, imitation-based driving planners have reported considerable success.
However, due to the absence of a standardized benchmark, the effectiveness of various …

Diffusion models excel at modeling complex and multimodal trajectory distributions for
decision-making and control. Reward-gradient guided denoising has been recently …

Planning an optimal route in a complex environment requires efficient reasoning about the
surrounding scene. While human drivers prioritize important objects and ignore details not …